I am trying to plot 38 plots using matplotlib.pyplot.subplots but the graphs come out too small and on top of each other.
N = np.linspace(3,40,38,dtype=int)
plt.figure(1, figsize=(12,2))
for i in N:
plt.subplot(38,1,i-2)
plt.plot(N,i*N)
plt.subplots_adjust(hspace = 5)
How do I modify this code so that I can see the individual plots clearly?
Related
I am looking for some option to automatically resize the figures that I am generating using seaborn (barplots, countplot, boxplot). I am creating all the plots in one shot, but the issue is, in some of the graphs labels & bars are tightly packed because some of the columns have too many categorical values. I am using the below code:
for col in dff.drop(target_col_name, axis=1).columns:
if ((dff[col].nunique() / len(dff[col])) < threshold):
ax = sns.countplot(x=dff[col], hue= dff[target_col_name] )
ax.set_xticklabels(ax.get_xticklabels(), rotation = 90)
plt.tight_layout()
plt.show()
pd.crosstab(index = dff[col],
columns = dff[target_col_name], normalize = 'index').plot.bar()
plt.tight_layout()
plt.show()
elif (dff[col].dtype == 'int64' or dff[col].dtype == 'float64'):
sns.boxplot(dff[target_col_name], dff[col])
One solution is to increase all the figsize for all figures or use another if condition to target specific columns that have more categorical values and increase the size of those figures.
But I am looking for a more flexible solution so that all the figures get resized automatically based on the information in them.
I have used a plotly in-built function "figure()" that you can use to alter the size of charts. All you need do is declare it right before the code for your chats.
For instance, plt.figure(figsize=(12,5)) alters the height and width of the chart to 12 and 5 respectively.
I would like to create a figure where subplots are added dynamically within a for-loop. It should be possible to define the width and height of each subplot in centimeters, that is, the more subplots are added, the bigger the figure needs to be to make room for 'incoming' subplots.
In my case, subplots should be added row-wise so that the figure has to get bigger in the y-dimension. I came across this stackoverflow post, which might lead in the right direction? Maybe also the gridspec module could solve this problem?
I tried out the code as described in the first post, but this couldn't solve my problem (it sets the final figure size, but the more subplots are added to the figure the smaller each subplot gets, as shown in this example):
import matplotlib.pyplot as plt
# set number of plots
n_subplots = 2
def set_size(w,h,ax=None):
""" w, h: width, height in inches """
if not ax: ax=plt.gca()
l = ax.figure.subplotpars.left
r = ax.figure.subplotpars.right
t = ax.figure.subplotpars.top
b = ax.figure.subplotpars.bottom
figw = float(w)/(r-l)
figh = float(h)/(t-b)
ax.figure.set_size_inches(figw, figh)
fig = plt.figure()
for idx in range(0,n_subplots):
ax = fig.add_subplot(n_subplots,1,idx+1)
ax.plot([1,3,2])
set_size(5,5,ax=ax)
plt.show()
You're setting the same figure size (5,5) regardless of the number of subplots. If I understood your question correctly, I think you want to set the height to be proportional to the number of subplots.
However, you'd be better off to create the figure with the right size from the get-go. The code that you are providing gives the correct layout only because you know before hand how many subplots your going to create (in fig.add_subplot(n_subplots,...)). If you are trying to add subplots without knowing the total number of subplot rows you need, the problem is more complicated.
n_subplots = 4
ax_w = 5
ax_h = 5
dpi = 100
fig = plt.figure(figsize=(ax_w, ax_h), dpi=dpi)
for idx in range(0,n_subplots):
ax = fig.add_subplot(n_subplots,1,idx+1)
ax.plot([1,3,2])
fig.set_size_inches(ax_w,ax_h*n_subplots)
fig.tight_layout()
I am reading Data from a Simulation out of an Excel File. Out of this Data I generated two DataFrames containing 200 values. Now i want to plot all the Values from DataFrame one in blue and all Values from DataFrame two in purple. Therefore I have following code:
df = pd.read_excel("###CENSORED####.xlsx", sheetname="Data")
unpatched = df["Unpatched"][:-800]
patched = df["Patched"][:-800]
x = range(0,len(unpatched))
fig = plt.figure(figsize=(10, 5))
plt.scatter(x, unpatched, zorder=10, )
plt.scatter(x, patched, c="purple",zorder=19,)
This results in following Graph:
But now i want to draw in some lines that visualize the difference between the blue and purple dots. I thought about an orange line going from blue dot at simulation-run x to the purple dot at simulation-run x. I've tried to "cheat" with following code, since I'm pretty new to matplotlib.
scale_factor = 300
for a in x:
plt.axvline(a, patched[a]/scale_factor, unpatched[a]/scale_factor, c="orange")
But this resulted in a inaccuracy as seen seen below:
So is there a smarter way to do this? I've realized that the axvline documentation only says that ymin, ymax can only be scalars. Can I somehow turn my given values into fitting scalars?
I've drawn a plot that looks something like the following:
It was created using the following code:
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
# 1. Plot a figure consisting of 3 separate axes
# ==============================================
plotNames = ['Plot1','Plot2','Plot3']
figure, axisList = plt.subplots(len(plotNames), sharex=True, sharey=True)
tempDF = pd.DataFrame()
tempDF['date'] = pd.date_range('2015-01-01','2015-12-31',freq='D')
tempDF['value'] = np.random.randn(tempDF['date'].size)
tempDF['value2'] = np.random.randn(tempDF['date'].size)
for i in range(len(plotNames)):
axisList[i].plot_date(tempDF['date'],tempDF['value'],'b-',xdate=True)
# 2. Create a new single axis in the figure. This new axis sits over
# the top of the axes drawn previously. Make all the components of
# the new single axis invisibe except for the x and y labels.
big_ax = figure.add_subplot(111)
big_ax.set_axis_bgcolor('none')
big_ax.set_xlabel('Date',fontweight='bold')
big_ax.set_ylabel('Random normal',fontweight='bold')
big_ax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
big_ax.spines['right'].set_visible(False)
big_ax.spines['top'].set_visible(False)
big_ax.spines['left'].set_visible(False)
big_ax.spines['bottom'].set_visible(False)
# 3. Plot a separate figure
# =========================
figure2,ax2 = plt.subplots()
ax2.plot_date(tempDF['date'],tempDF['value2'],'-',xdate=True,color='green')
ax2.set_xlabel('Date',fontweight='bold')
ax2.set_ylabel('Random normal',fontweight='bold')
# Save plot
# =========
plt.savefig('tempPlot.png',dpi=300)
Basically, the rationale for plotting the whole picture is as follows:
Create the first figure and plot 3 separate axes using a loop
Plot a single axis in the same figure to sit on top of the graphs
drawn previously. Label the x and y axes. Make all other aspects of
this axis invisible.
Create a second figure and plot data on a single axis.
The plot displays just as I want when using jupyter-notebook but when the plot is saved, the file contains only the second figure.
I was under the impression that plots could have multiple figures and that figures could have multiple axes. However, I suspect I have a fundamental misunderstanding of the differences between plots, subplots, figures and axes. Can someone please explain what I'm doing wrong and explain how to get the whole image to save to a single file.
Matplotlib does not have "plots". In that sense,
plots are figures
subplots are axes
During runtime of a script you can have as many figures as you wish. Calling plt.save() will save the currently active figure, i.e. the figure you would get by calling plt.gcf().
You can save any other figure either by providing a figure number num:
plt.figure(num)
plt.savefig("output.png")
or by having a refence to the figure object fig1
fig1.savefig("output.png")
In order to save several figures into one file, one could go the way detailed here: Python saving multiple figures into one PDF file.
Another option would be not to create several figures, but a single one, using subplots,
fig = plt.figure()
ax = plt.add_subplot(611)
ax2 = plt.add_subplot(612)
ax3 = plt.add_subplot(613)
ax4 = plt.add_subplot(212)
and then plot the respective graphs to those axes using
ax.plot(x,y)
or in the case of a pandas dataframe df
df.plot(x="column1", y="column2", ax=ax)
This second option can of course be generalized to arbitrary axes positions using subplots on grids. This is detailed in the matplotlib user's guide Customizing Location of Subplot Using GridSpec
Furthermore, it is possible to position an axes (a subplot so to speak) at any position in the figure using fig.add_axes([left, bottom, width, height]) (where left, bottom, width, height are in figure coordinates, ranging from 0 to 1).
I'm having trouble giving colorbars to a grid of line plots in Matplotlib.
I have a grid of plots, which each shows 64 lines. The lines depict the penalty value vs time when optimizing the same system under 64 different values of a certain hyperparameter h.
Since there are so many lines, instead of using a standard legend, I'd like to use a colorbar, and color the lines by the value of h. In other words, I'd like something that looks like this:
The above was done by adding a new axis to hold the colorbar, by calling figure.add_axes([0.95, 0.2, 0.02, 0.6]), passing in the axis position explicitly as parameters to that method. The colorbar was then created as in the example code here, by instantiating a ColorbarBase(). That's fine for single plots, but I'd like to make a grid of plots like the one above.
To do this, I tried doubling the number of subplots, and using every other subplot axis for the colorbar. Unfortunately, this led to the colorbars having the same size/shape as the plots:
Is there a way to shrink just the colorbar subplots in a grid of subplots like the 1x2 grid above?
Ideally, it'd be great if the colorbar just shared the same axis as the line plot it describes. I saw that the colorbar.colorbar() function has an ax parameter:
ax
parent axes object from which space for a new colorbar axes will be stolen.
That sounds great, except that colorbar.colorbar() requires you to pass in a imshow image, or a ContourSet, but my plot is neither an image nor a contour plot. Can I achieve the same (axis-sharing) effect using ColorbarBase?
It turns out you can have different-shaped subplots, so long as all the plots in a given row have the same height, and all the plots in a given column have the same width.
You can do this using gridspec.GridSpec, as described in this answer.
So I set the columns with line plots to be 20x wider than the columns with color bars. The code looks like:
grid_spec = gridspec.GridSpec(num_rows,
num_columns * 2,
width_ratios=[20, 1] * num_columns)
colormap_type = cm.cool
for (x_vec_list,
y_vec_list,
color_hyperparam_vec,
plot_index) in izip(x_vec_lists,
y_vec_lists,
color_hyperparam_vecs,
range(len(x_vecs))):
line_axis = plt.subplot(grid_spec[grid_index * 2])
colorbar_axis = plt.subplot(grid_spec[grid_index * 2 + 1])
colormap_normalizer = mpl.colors.Normalize(vmin=color_hyperparam_vec.min(),
vmax=color_hyperparam_vec.max())
scalar_to_color_map = mpl.cm.ScalarMappable(norm=colormap_normalizer,
cmap=colormap_type)
colorbar.ColorbarBase(colorbar_axis,
cmap=colormap_type,
norm=colormap_normalizer)
for (line_index,
x_vec,
y_vec) in zip(range(len(x_vec_list)),
x_vec_list,
y_vec_list):
hyperparam = color_hyperparam_vec[line_index]
line_color = scalar_to_color_map.to_rgba(hyperparam)
line_axis.plot(x_vec, y_vec, color=line_color, alpha=0.5)
For num_rows=1 and num_columns=1, this looks like: